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Welcome to the Ocean Color Bio-optical Algorithm Mini Workshop Goals, Motivation, and Guidance Janet W. Campbell University of New Hampshire Durham, New.

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Presentation on theme: "Welcome to the Ocean Color Bio-optical Algorithm Mini Workshop Goals, Motivation, and Guidance Janet W. Campbell University of New Hampshire Durham, New."— Presentation transcript:

1 Welcome to the Ocean Color Bio-optical Algorithm Mini Workshop Goals, Motivation, and Guidance Janet W. Campbell University of New Hampshire Durham, New Hampshire September 27, 2005

2 This workshop is aimed at evaluating ocean color algorithms that produce chlorophyll retrievals. It is expected that the algorithms tested may also retrieve other constituents and / or related inherent optical properties. Our goal is to determine how new algorithms perform compared to the operational empirical algorithms (OC4, OC3M) and to evaluate whether improved accuracy is achieved by accounting for other optically active constituents. Goals

3  NOMAD. We have a new data set to use in evaluating algorithms. Motivation Jeremy Werdell will present overview of NOMAD.

4  NOMAD. We have a new data set to use in evaluating algorithms.  The operational algorithms used for SeaWiFS (OC4) and MODIS (OC3M) are not mutually consistent. Motivation

5 MODIS is currently producing the “SeaWiFS-analog” chlorophyll product. It employs the OC3M algorithm parameterized with the same data set used for the SeaWiFS OC4.v4 algorithm (n = 2,804). OC3M Both are described in NASA TM , Vol. 11 (O’Reilly et al., 2000).

6 RMSE = (SeaBAM 0.184; AMT 0.256; W. Fla. Shelf 0.175; Ches ) Approach: Test algorithms with in situ data and later with satellite match-ups. Four in situ data sets of reflectance and chlorophyll data shown here (n = 1,119).

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8 The SeaWiFS algorithm (OC4.v4) is: log 10 (CHL) = – 3.067R R R 3 – 1.532R 4 where R = log 10 [max(R rs (443), R rs (490), R rs (510))/ R rs (555)] The algorithms are different… The MODIS algorithm (OC3M) is: log 10 (CHL) = – 2.753R R R 3 – 1.403R 4 where R = log 10 [max(R rs (443), R rs (488))/ R rs (551)]

9 There are systematic differences between the OC3M and OC4 algorithms even when applied to the same data set (assuming 448 ~ 490, 551 ~ 555) The MODIS chlorophylls will be slightly less over most of the ocean, i.e., where Chl < 3 mg m -3. The algorithms are not the same even when using the 443:550 ratio. Differences were intentional to account for differences in spectral responses of the MODIS and SeaWiFS bands and also fact that 488 ≠ 490 and 551 ≠ 555.

10  NOMAD. We have a new data set to use in evaluating algorithms.  The operational algorithms used for SeaWiFS (OC4) and MODIS (OC3M) are not mutually consistent. It is desireable to have an algorithm that can be applied to different sensors to facilitate the generation of Climate Data Records (CDRs). Motivation

11  NOMAD. We have a new data set to use in evaluating algorithms.  The operational algorithms used for SeaWiFS (OC4) and MODIS (OC3M) are not mutually consistent. It is desireable to have an algorithm that can be applied to different sensors to facilitate the generation of Climate Data Records (CDRs).  A paper is about to appear in Geophysical Research Letters arguing for the importance of accounting for the effects of colored dissolved organic matter. Motivation

12 This paper, entitled “Colored dissolved organic matter and its influence on the satellite-based characterization of the ocean biosphere” by D. Siegel, S. Maritorena, N. Nelson, M. Behrenfeld, and C. McClain, is in press in Geophysical Research Letters. The authors applied the GSM01 algorithm to global SeaWiFS data and compared the derived chlorophyll distributions with OC4 chlorophyll maps. Differences are quite significant – and this paper will predictably cause a stir. We should be prepared to respond … Stephane Maritorean will present overview of this paper.

13 Any algorithm approach may be considered. This is not a workshop to look only at “semi-analytical” algorithms. Algorithms will be evaluated using a common data set (a subset of NOMAD) and common performance criteria. Don’t present results for someone else’s algorithm unless you’re sure it is implemented correctly. We should regard the OC4 and OC3M as the “algorithms to beat.” Compare your results with these operational algorithms, not with other non-operational ones. Guidance (Rules of Engagement)

14 If we can resolve other optical properties or constituents (e.g., CDOM, POC) all the better … but our focus should remain on chlorophyll. Ideally, we should eliminate systematic differences between SeaWiFS and MODIS chlorophyll algorithms. The challenge is to explain and reduce the errors in Chl with an algorithm that can be implemented practically. Issues related to its practical application include its speed, sensitivity to errors in L wn, and ability to converge on a solution.

15 Apparent Optical Properties Radiance Reflectance Optically Active Constituents Pigments (Chl), Sediment, CDOM Empirical algorithms Analytical algorithms Empirical parameterizations Inherent Optical Properties Absorption Scattering

16 Model-based Algorithms orward model: F orward model: L( ) = f(C,  ( )) L( ) : An ocean color spectrum (reflectance, radiance) L( ) = f(C,  ( )) L( ) : An ocean color spectrum (reflectance, radiance) f : A semi-analytical “forward” model f : A semi-analytical “forward” model C : Optically-active constituent vector C : Optically-active constituent vector  ( ) : A model parameter vector related to IOP models  ( ) : A model parameter vector related to IOP models nverse model: the retrieval of C I nverse model: the retrieval of C C = f -1 (L( ),  ( ) ) C = f -1 (L( ),  ( ) ) f -1 : An inverse of f ( an approach ) f -1 : An inverse of f ( an approach )

17 Model-based Algorithms The forward model can actually be a simulation model (e.g., Hydrolight). Whatever it is, it should be tested with empirical data. How accurate should the forward model be? The inverse model is what we call the algorithm. We tend to think of “semi-analytic” algorithms inverted by linear or non-linear optimization techniques. But the inversion approach can be highly statistical (e.g., neural network). If it is trained with model-generated data, then this type of algorithm is also a “model-based algorithm.”

18 Ocean Color Bio-optical Algorithm Mini-Workshop (OCBAM) Penobscot Room, New England Center University of New Hampshire Durham, New Hampshire Tuesday, September 27, :00Welcoming remarks – 9:15Overview talks Goals, motivation, and guidance – Janet Campbell CDOM & its influence on satellite chlorophyll – Stephane Maritorena Discussion 10:30Break 11:00Framework Brief background on SeaBAM methods – Jay O’Reilly Performance criteria for algorithms – Janet Campbell The NOMAD dataset – Jeremy Werdell 12 noon – Lunch (NEC dining room)

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